Category: Data Science

Author: Vincent Granville Many statistics, such as correlations or R-squared, depend on the sample size, making it difficult to compare values computed on two data […] Read More

Author: Vincent Granville Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for […] Read More

Author: Manu Jeevan If you are looking to transition your career to data science, don’t immediately start learning Python or R. Instead, leverage the domain […] Read More

Author: Stephanie Glen R-squared measures how well your data fits a regression line. More specifically, it’s how much variation in the response variable your linear model […] Read More

Author: Matt Mayo Editor Backpropagation is one of those topics that seem to confuse many once you move past feed-forward neural networks and progress to […] Read More

Author: William Vorhies Summary:  Real Time Computer Vision (RTCV) that requires processing video DNNs at the edge is likely to be the next killer app […] Read More

Author: Manu Jeevan Also: The 3 Biggest Mistakes on Learning Data Science; A gallery of interesting Jupyter Notebooks; How do you teach physics to machine […] Read More

Author: ajit jaokar After testing this idea for the last few months, we have formally launched this concept   The idea of ‘Data Science Coding […] Read More

Author: Matt Mayo Editor Don’t miss Canada’s #1 data, AI and analytics conference + expo. From solving your data-driven business challenges to helping you navigate […] Read More

Author: Vincent Granville Here is a simple trick that can solve a lot of problems. You can not trust a linear or logistic regression performed […] Read More